shape for dimensions in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of shape attribute on DataFrame

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

print(df.shape)

A (9, 1)

B (3,)

C (1, 9)

D (3, 3)

💡 Hint

The shape attribute returns a tuple with (rows, columns).

The DataFrame has 3 rows and 3 columns, so df.shape returns (3, 3).

❓ data_output

intermediate

Shape of a Series object

What is the output of the shape attribute for the following pandas Series?

Pandas

import pandas as pd

s = pd.Series([10, 20, 30, 40])
print(s.shape)

A (1, 4)

B 4

C (4,)

D (4, 1)

💡 Hint

Series shape is a tuple with one element representing length.

A pandas Series is one-dimensional, so its shape is a tuple with one element: (4,).

🔧 Debug

advanced

Error when accessing shape of a list

What error will this code produce?

Pandas

my_list = [1, 2, 3, 4]
print(my_list.shape)

A AttributeError: 'list' object has no attribute 'shape'

B TypeError: 'list' object is not callable

C NameError: name 'shape' is not defined

D IndexError: list index out of range

💡 Hint

Lists in Python do not have a shape attribute.

Python lists do not have a shape attribute, so trying to access it raises an AttributeError.

🧠 Conceptual

advanced

Understanding shape for 3D numpy arrays

If a numpy array has shape (4, 3, 2), what does each number represent?

A 4 layers, 3 rows, 2 columns

B 4 rows, 3 columns, 2 layers

C 4 columns, 3 rows, 2 layers

D 4 rows, 2 columns, 3 layers

💡 Hint

Shape is given as (layers, rows, columns) for 3D arrays.

For a 3D numpy array, the shape is (layers, rows, columns). So (4, 3, 2) means 4 layers, each with 3 rows and 2 columns.

🚀 Application

expert

Determining shape after DataFrame filtering

Given the DataFrame below, what is the shape after filtering rows where 'Age' > 30?

Pandas

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara', 'Dave'],
        'Age': [25, 35, 45, 28],
        'City': ['NY', 'LA', 'NY', 'SF']}
df = pd.DataFrame(data)

filtered_df = df[df['Age'] > 30]
print(filtered_df.shape)

A (3, 3)

B (2, 3)

C (2, 2)

D (3, 2)

💡 Hint

Count how many rows have Age greater than 30 and remember columns stay the same.

Only 'Bob' and 'Cara' have Age > 30, so 2 rows remain. The DataFrame has 3 columns, so shape is (2, 3).

Practice

1. What does the shape attribute of a pandas DataFrame tell you?

easy

A. The data types of each column

B. The summary statistics of the data

C. The first few rows of the DataFrame

D. The number of rows and columns as a tuple (rows, columns)

Solution

1. Step 1: Understand what shape returns

   The shape attribute gives the size of the DataFrame as a tuple showing rows and columns.

2. Step 2: Compare options with this definition

   Only The number of rows and columns as a tuple (rows, columns) correctly describes shape as (rows, columns).

3. Final Answer:

   The number of rows and columns as a tuple (rows, columns) -> Option D

4. Quick Check:

   shape = (rows, columns) [OK]

Hint: Remember shape shows (rows, columns) size [OK]

Common Mistakes:

• Confusing shape with data types
• Thinking shape shows data preview
• Mixing shape with summary statistics

2. Which of the following is the correct way to get the shape of a pandas DataFrame named df?

easy

A. df.shape()

B. df.shape

C. df.get_shape()

D. shape(df)

Solution

1. Step 1: Recall how to access attributes in pandas

   The shape is an attribute, not a method, so no parentheses are used.

2. Step 2: Check each option

   df.shape uses df.shape correctly without parentheses. Others are incorrect syntax.

3. Final Answer:

   df.shape -> Option B

4. Quick Check:

   Use attribute without () for shape [OK]

Hint: Use attribute without parentheses for shape [OK]

Common Mistakes:

• Adding parentheses like a function call
• Using non-existent methods like get_shape()
• Trying to call shape as a standalone function

3. Given the following code:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
print(df.shape)

What will be the output?

medium

A. (3, 2)

B. (2, 3)

C. [3, 2]

D. Error

Solution

1. Step 1: Understand the DataFrame structure

   The dictionary has 2 keys (columns) and each key has 3 values (rows).

2. Step 2: Determine the shape

   So the DataFrame has 3 rows and 2 columns, shape is (3, 2).

3. Final Answer:

   (3, 2) -> Option A

4. Quick Check:

   Rows = 3, Columns = 2 [OK]

Hint: Count rows then columns for shape tuple [OK]

Common Mistakes:

• Swapping rows and columns order
• Using list brackets instead of tuple
• Expecting an error from correct code

4. You run this code:

import pandas as pd
df = pd.DataFrame({'X': [1, 2], 'Y': [3, 4]})
print(df.shape())

What is the problem?

medium

A. Column names are invalid

B. DataFrame is empty

C. Using parentheses with shape causes an error

D. Missing import statement

Solution

1. Step 1: Identify how shape is accessed

   shape is an attribute, not a method, so it should not have parentheses.

2. Step 2: Understand the error cause

   Calling df.shape() tries to call a tuple as a function, causing a TypeError.

3. Final Answer:

   Using parentheses with shape causes an error -> Option C

4. Quick Check:

   Access shape without () to avoid error [OK]

Hint: Don't add () after shape attribute [OK]

Common Mistakes:

• Treating shape like a function
• Assuming empty DataFrame causes error
• Ignoring import errors

5. You have a pandas Series s = pd.Series([10, 20, 30, 40]). What will s.shape return?

hard

A. (4,)

B. (4, 1)

C. 4

D. Error

Solution

1. Step 1: Understand Series shape

   A pandas Series is one-dimensional, so its shape is a tuple with one element: the number of items.

2. Step 2: Check the length of the Series

   There are 4 elements, so shape is (4,).

3. Final Answer:

   (4,) -> Option A

4. Quick Check:

   Series shape is single-element tuple (length,) [OK]

Hint: Series shape is a one-item tuple with length [OK]

Common Mistakes:

• Expecting a 2D shape tuple
• Returning just an integer instead of tuple
• Thinking shape causes error on Series